.ipynb .pdf

Binder

Contents

Flight tracks

This example demonstrates how to use the (AC)³airborne flight segments and plots all flight on an interactive map.

Data access

• To load the data we first load the (AC)³airborne meta data catalogue. More information on the catalog can be found here.

import ac3airborne

cat = ac3airborne.get_intake_catalog()
tracks = list(cat.Polar5.GPS_INS)
tracks

['ACLOUD_P5_RF04',
 'ACLOUD_P5_RF05',
 'ACLOUD_P5_RF06',
 'ACLOUD_P5_RF07',
 'ACLOUD_P5_RF08',
 'ACLOUD_P5_RF10',
 'ACLOUD_P5_RF11',
 'ACLOUD_P5_RF13',
 'ACLOUD_P5_RF14',
 'ACLOUD_P5_RF15',
 'ACLOUD_P5_RF16',
 'ACLOUD_P5_RF17',
 'ACLOUD_P5_RF18',
 'ACLOUD_P5_RF19',
 'ACLOUD_P5_RF20',
 'ACLOUD_P5_RF21',
 'ACLOUD_P5_RF22',
 'ACLOUD_P5_RF23',
 'ACLOUD_P5_RF25',
 'AFLUX_P5_RF02',
 'AFLUX_P5_RF03',
 'AFLUX_P5_RF04',
 'AFLUX_P5_RF05',
 'AFLUX_P5_RF06',
 'AFLUX_P5_RF07',
 'AFLUX_P5_RF08',
 'AFLUX_P5_RF09',
 'AFLUX_P5_RF10',
 'AFLUX_P5_RF11',
 'AFLUX_P5_RF12',
 'AFLUX_P5_RF13',
 'AFLUX_P5_RF14',
 'AFLUX_P5_RF15',
 'MOSAiC-ACA_P5_RF02',
 'MOSAiC-ACA_P5_RF03',
 'MOSAiC-ACA_P5_RF04',
 'MOSAiC-ACA_P5_RF05',
 'MOSAiC-ACA_P5_RF06',
 'MOSAiC-ACA_P5_RF07',
 'MOSAiC-ACA_P5_RF08',
 'MOSAiC-ACA_P5_RF09',
 'MOSAiC-ACA_P5_RF10',
 'MOSAiC-ACA_P5_RF11']

Load Polar 5 flight phase information

Polar 5 flights are divided into segments to easily access start and end times of flight patterns. For more information have a look at the respective github repository.

At first we want to load the flight segments of (AC)³airborne

meta = ac3airborne.get_flight_segments()

Plot

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import ipyleaflet
from simplification.cutil import simplify_coords_idx

To plot all flight segments, we define some helper functions

def track2layer(track, color="green", name=""):
    return ipyleaflet.Polyline(
        locations=np.stack([track.lat.values, track.lon.values], axis=1).tolist(), 
        color=color,
        fill=False,
        weight=2,
        name=name
    )

def simplify_dataset(ds, tolerance):
    indices_to_take = simplify_coords_idx(np.stack([ds.lat.values, ds.lon.values], axis=1), tolerance)
    return ds.isel(time=indices_to_take)

# define colors for the flight tracks
colors = [matplotlib.colors.to_hex(c)
          for c in plt.cm.inferno(np.linspace(0, 1, len(tracks)))]

m = ipyleaflet.Map(
    basemap=ipyleaflet.basemaps.Esri.NatGeoWorldMap,
    center=(80., 6), zoom=3
)

print(tracks)

['ACLOUD_P5_RF04', 'ACLOUD_P5_RF05', 'ACLOUD_P5_RF06', 'ACLOUD_P5_RF07', 'ACLOUD_P5_RF08', 'ACLOUD_P5_RF10', 'ACLOUD_P5_RF11', 'ACLOUD_P5_RF13', 'ACLOUD_P5_RF14', 'ACLOUD_P5_RF15', 'ACLOUD_P5_RF16', 'ACLOUD_P5_RF17', 'ACLOUD_P5_RF18', 'ACLOUD_P5_RF19', 'ACLOUD_P5_RF20', 'ACLOUD_P5_RF21', 'ACLOUD_P5_RF22', 'ACLOUD_P5_RF23', 'ACLOUD_P5_RF25', 'AFLUX_P5_RF02', 'AFLUX_P5_RF03', 'AFLUX_P5_RF04', 'AFLUX_P5_RF05', 'AFLUX_P5_RF06', 'AFLUX_P5_RF07', 'AFLUX_P5_RF08', 'AFLUX_P5_RF09', 'AFLUX_P5_RF10', 'AFLUX_P5_RF11', 'AFLUX_P5_RF12', 'AFLUX_P5_RF13', 'AFLUX_P5_RF14', 'AFLUX_P5_RF15', 'MOSAiC-ACA_P5_RF02', 'MOSAiC-ACA_P5_RF03', 'MOSAiC-ACA_P5_RF04', 'MOSAiC-ACA_P5_RF05', 'MOSAiC-ACA_P5_RF06', 'MOSAiC-ACA_P5_RF07', 'MOSAiC-ACA_P5_RF08', 'MOSAiC-ACA_P5_RF09', 'MOSAiC-ACA_P5_RF10', 'MOSAiC-ACA_P5_RF11']

for flight_id,color in zip(tracks,colors):
    ds = cat.Polar5.GPS_INS[flight_id].to_dask()
    dsreduced = simplify_dataset(
        ds.sel(time=slice(meta['P5'][flight_id]['takeoff'],meta['P5'][flight_id]['landing'])), 1e-5)
    m.add_layer(track2layer(dsreduced,color))

m.add_control(ipyleaflet.ScaleControl(position='bottomleft'))
m.add_control(ipyleaflet.LegendControl(dict(zip(tracks, colors)),
                                       name="Flights",
                                       position="bottomright"))
m.add_control(ipyleaflet.LayersControl(position='topright'))
m.add_control(ipyleaflet.FullScreenControl())
display(m)

previous

Querying the flight phase files

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land-ocean mask